Nevin Manimala

Imaging Neurosci (Camb). 2024 Oct 3;2:imag-2-00306. doi: 10.1162/imag_a_00306. eCollection 2024.

ABSTRACT

T1-weighted imaging holds wide applications in clinical and research settings; however, the challenge of inter-scanner variability arises when combining data across scanners, which impedes multi-site research. To address this, post-acquisition harmonization methods such as statistical or deep learning approaches have been proposed to unify cross-scanner images. Nevertheless, how inter-scanner variability manifests in images and derived measures, and how to harmonize it in an interpretable manner, remains underexplored. To broaden our knowledge of inter-scanner variability and leverage it to develop a new harmonization strategy, we devised a pipeline to assess the interpretable inter-scanner variability in matched T1-weighted images across four 3T MRI scanners. The pipeline incorporates ComBat modeling with 3D superpixel parcellation algorithm (namely SP-ComBat), which estimates location and scale effects to quantify the shift and spread in relative signal distributions, respectively, concerning brain tissues in the image domain. The estimated parametric maps revealed significant contrast deviations compared to the joint signal distribution across scanners (p< 0.001), and the identified deviations in signal intensities may relate to differences in the inversion time acquisition parameter. To reduce the inter-scanner variability, we implemented a harmonization strategy involving proper image preprocessing and site effect removal by ComBat-derived parameters, achieving substantial improvement in image quality and significant reduction in variation of volumetric measures of brain tissues (p< 0.001). We also applied SP-ComBat to evaluate and characterize the performance of various image harmonization techniques, demonstrating a new way to assess image harmonization. In addition, we reported various metrics of T1-weighted images to quantify the impact of inter-scanner variation, including signal-to-noise ratio, contrast-to-noise ratio, signal inhomogeneity index, and structural similarity index. This study demonstrates a pipeline that extends the implementation of statistical ComBat method to the image domain in a practical manner for characterizing and harmonizing the inter-scanner variability in T1-weighted images, providing further insight for the studies focusing on the development of image harmonization methodologies and their applications.

PMID:40800451 | PMC:PMC12290534 | DOI:10.1162/imag_a_00306

Imaging Neurosci (Camb). 2024 Oct 25;2:imag-2-00328. doi: 10.1162/imag_a_00328. eCollection 2024.

ABSTRACT

The serotonin 1A receptor has been linked to both the pathophysiology of major depressive disorder (MDD) and the antidepressant action of serotonin reuptake inhibitors. Most PET studies of the serotonin 1A receptor in MDD used the receptor antagonist radioligand, [carbonyl- ${}_{}{}^{11}\text{C}$ ]WAY100635; however, the interpretation of the combined results has been contentious owing to reports of higher or lower binding in MDD with different outcome measures. The reasons for these divergent results originate from several sources, including properties of the radiotracer itself, which complicate its quantification and interpretation; as well as from previously reported differences between MDD and healthy volunteers in both reference tissue binding and plasma-free fraction, which are typically assumed not to differ. Recently, we have developed two novel hierarchical multivariate methods which we validated for the quantification and analysis of [ ${}_{}{}^{11}\text{C}$ ]WAY100635, which show better accuracy and inferential efficiency compared to standard analysis approaches. Importantly, these new methods should theoretically be more resilient to many of the factors thought to have caused the discrepancies observed in previous studies. We sought to apply these methods in the largest [ ${}_{}{}^{11}\text{C}$ ]WAY100635 sample to date, consisting of 160 individuals, including 103 MDD patients, of whom 50 were not-recently-medicated and 53 were antidepressant-exposed, as well as 57 healthy volunteers. While the outcome measure discrepancies were substantial using conventional univariate analysis, our multivariate analysis techniques instead yielded highly consistent results across PET outcome measures and across pharmacokinetic models, with all approaches showing higher serotonin 1A autoreceptor binding potential in the raphe nuclei of not-recently-medicated MDD patients relative to both healthy volunteers and antidepressant-exposed MDD patients. Moreover, with the additional precision of estimates afforded by this approach, we can show that while binding is also higher in projection areas in this group, these group differences are approximately half of those in the raphe nuclei, which are statistically distinguishable from one another. These results are consistent with the biological role of the serotonin 1A autoreceptor in the raphe nuclei in regulating serotonin neuron firing and release, and with preclinical and clinical evidence of deficient serotonin activity in MDD due to over-expression of autoreceptors resulting from genetic and/or epigenetic effects. These results are also consistent with downregulation of autoreceptors as a mechanism of action of selective serotonin reuptake inhibitors. In summary, the results using multivariate analysis approaches, therefore, demonstrate both face and convergent validity, and may serve to provide a resolution and consensus interpretation for the disparate results of previous studies examining the serotonin 1A receptor in MDD.

PMID:40800447 | PMC:PMC12290790 | DOI:10.1162/imag_a_00328

Imaging Neurosci (Camb). 2024 May 20;2:imag-2-00177. doi: 10.1162/imag_a_00177. eCollection 2024.

ABSTRACT

Since the inception of magnetization transfer (MT) imaging, it has been widely assumed that Henkelman’s two spin pools have similar longitudinal relaxation times, which motivated many researchers to constrain them to each other. However, several recent publications reported a $T_1^s$ of thesemi-solid spin poolthat is much shorter than $T_1^f$ of thefree pool. While these studies tailored experiments for robust proofs-of-concept, we here aim to quantify the disentangled relaxation processes on a voxel-by-voxel basis in a clinical imaging setting, that is, with an effective resolution of 1.24mm isotropic and full brain coverage in 12min. To this end, we optimized ahybrid-statepulse sequence for mapping the parameters of an unconstrained MT model. We scanned four people with relapsing-remitting multiple sclerosis (MS) and four healthy controls with this pulse sequence and estimated $T_1^f\approx 1.84s$ and $T_1^s\approx 0.34s$ in healthy white matter. Our results confirm the reports that $T_1^s\ll T_1^f$ and we argue that this finding identifies MT as an inherent driver of longitudinal relaxation in brain tissue. Moreover, we estimated a fractional size of the semi-solid spin pool of $m_0^s\approx 0.212$ , which is larger than previously assumed. An analysis of $T_1^f$ in normal-appearing white matter revealed statistically significant differences between individuals with MS and controls.

PMID:40800438 | PMC:PMC12247553 | DOI:10.1162/imag_a_00177

Imaging Neurosci (Camb). 2024 Feb 2;2:imag-2-00071. doi: 10.1162/imag_a_00071. eCollection 2024.

ABSTRACT

Recent evidence suggests brain-phenotype predictions may require very large sample sizes. However, as the sample size increases, missing data also increase. Conventional methods, like complete-case analysis, discard useful information and shrink the sample size. To address the missing data problem, we investigated rescuing these missing data through imputation. Imputation is substituting estimated values for missing data to be used in downstream analyses. We integrated imputation methods into the Connectome-based Predictive Modeling (CPM) framework. Utilizing four open-source datasets-the Human Connectome Project, the Philadelphia Neurodevelopmental Cohort, the UCLA Consortium for Neuropsychiatric Phenomics, and the Healthy Brain Network (HBN)-we validated and compared our framework with different imputation methods against complete-case analysis for both missing connectomes and missing phenotypic measures scenarios. Imputing connectomes exhibited superior prediction performance on real and simulated missing data compared to complete-case analysis. In addition, we found that imputation accuracy was a good indicator for choosing an imputation method for missing phenotypic measures but not informative for missing connectomes. In a real-world example predicting cognition using the HBN, we rescued 628 individuals through imputation, doubling the complete case sample size and increasing the variance explained by the predicted value by 45%. In conclusion, our study is a benchmark for state-of-the-art imputation techniques when dealing with missing connectome and phenotypic data in predictive modeling scenarios. Our results suggest that improving prediction performance can be achieved by strategically addressing missing data through effective imputation methods rather than resorting to the outright exclusion of participants. Our results suggest that rescuing data with imputation, instead of discarding participants with missing information, improves prediction performance.

PMID:40800425 | PMC:PMC12224408 | DOI:10.1162/imag_a_00071

Imaging Neurosci (Camb). 2024 Oct 7;2:imag-2-00303. doi: 10.1162/imag_a_00303. eCollection 2024.

ABSTRACT

Mapping of human brain structural connectomes via diffusion magnetic resonance imaging (dMRI) offers a unique opportunity to understand brain structural connectivity and relate it to various human traits, such as cognition. However, head displacement during image acquisition can compromise the accuracy of connectome reconstructions and subsequent inference results. We develop a generative model to learn low-dimensional representations of structural connectomes invariant to motion-induced artifacts, so that we can link brain networks and human traits more accurately, and generate motion-adjusted connectomes. We apply the proposed model to data from the Adolescent Brain Cognitive Development (ABCD) study and the Human Connectome Project (HCP) to investigate how our motion-invariant connectomes facilitate understanding of the brain network and its relationship with cognition. Empirical results demonstrate that the proposed motion-invariant variational autoencoder (inv-VAE) outperforms its competitors in various aspects. In particular, motion-adjusted structural connectomes are more strongly associated with a wide array of cognition-related traits than other approaches without motion adjustment.

PMID:40800413 | PMC:PMC12290590 | DOI:10.1162/imag_a_00303

Imaging Neurosci (Camb). 2024 Jun 24;2:imag-2-00188. doi: 10.1162/imag_a_00188. eCollection 2024.

ABSTRACT

White matter hyperintensities (WMH) reflect cerebral small vessel disease (cSVD), a major brain pathology contributing to cognitive decline and dementia. Vascular risk factors, including higher diastolic blood pressure (DBP), have been associated with the progression of WMH yet longitudinal studies have not comprehensively assessed these effects for abdominal obesity or reported sex/gender-specific effects. In this pre-registered analysis of a longitudinal population-based neuroimaging cohort, we investigated the association of baseline DBP and waist-to-hip ratio with WMH progression in linear mixed models. We also examined the relationship of WMH progression and executive and global cognitive function. We conducted gender interaction and stratified analyses. We included data from 596 individuals (44.1 % females, mean age = 63.2 years) with two MRI scans over approximately 6 years. We did not find a significant association of baseline DBP with WMH progression. WMH progression significantly predicted global cognitive decline but not decline in executive function. In exploratory analyses, increases in DBP as well as baseline and increase in systolic blood pressure were associated with WMH progression, confined to frontal periventricular regions. There was no association of WHR nor any gender-specific associations with WMH progression. Adequate BP control might contribute to limit WMH progression and negative effects on global cognitive function in the middle-aged to older population for men and women.

PMID:40800400 | PMC:PMC12272209 | DOI:10.1162/imag_a_00188

Imaging Neurosci (Camb). 2024 Nov 14;2:imag-2-00372. doi: 10.1162/imag_a_00372. eCollection 2024.

ABSTRACT

Currently, whole-brain vertex-wise analyses on brain surfaces commonly require specially configured operating systems/environments to run and are largely inaccessible to R users. As such, these analyses are inconvenient to execute and inaccessible to many aspiring researchers. To address these limitations, we present VertexWiseR, a user-friendly R package, to run cortical and hippocampal surface vertex-wise analyses, in just about any computer, requiring minimal technical expertise and computational resources. The package allows cohort-wise anatomical surface data to be highly compressed into a single, compact, easy-to-share file. Users can then run a range of vertex-wise statistical analyses with that single file without requiring a special operating system/environment and direct access to the preprocessed file directories. This enables the user to easily take the analyses “offline”, which would be highly appropriate and conducive in classroom settings. This R package includes a conventional suite of tools for extracting, manipulating, analyzing, and visualizing vertex-wise data, and is designed to be easy for beginners to use. Furthermore, it also contains novel or advanced functionalities such as hippocampal surface analyses, meta-analytic decoding, threshold-free cluster enhancement, and mixed-effects models that would appeal to experienced researchers as well. In the current report, we showcase these functionalities in the analyses of two publicly accessible datasets. Overall, our R package opens up new frontiers for the R’s user base/community and makes such neuroimaging analyses accessible to the masses.

PMID:40800380 | PMC:PMC12330379 | DOI:10.1162/imag_a_00372

Commun Biol. 2025 Aug 12;8(1):1207. doi: 10.1038/s42003-025-08581-z.

ABSTRACT

One of the most common objectives in the analysis of flow cytometry data is the identification and delineation of phenotypes, distinct populations of cells with shared characteristics in the measurement dimensions. We have developed an automated tool to comprehensively identify these cell populations by Exhaustive Projection Pursuit (EPP). The method evaluates all two-dimensional projections among the suitable data dimensions and creates an optimized sequence of statistically significant gating regions that identify all phenotypes supported by the data. We evaluate the results of EPP on four well characterized data sets from the literature. The C++ code for EPP can be called from any computing environment. We illustrate this with a MATLAB utility that integrates EPP with FlowJo. All source code is freely available.

PMID:40797028 | DOI:10.1038/s42003-025-08581-z

Sci Rep. 2025 Aug 12;15(1):29496. doi: 10.1038/s41598-025-15371-4.

ABSTRACT

Taraxacum officinale has been identified as a potential rare earth elements (REEs) accumulator, making it a promising bioindicator for urban environment. However, the influence of seasonal variation on the bioavailability, transport, and accumulation of REEs in plant tissues remains poorly understood. This knowledge gap is crucial, especially in the context of development of reliable bioindicators for urban pollution and managing urban ecosystems sustainably. The aim of this study was to evaluate seasonal changes in the content and distribution of Sc, Y and 14REEs in soils and dandelion roots and leaves from urban areas. Ten research sites typical for urbanised areas were designated and samples of soils and plants were collected in the spring and autumn season. The assessment of the level of studied elements combined with statistical analysis was performed. Variation in REEs accumulation in soil was found. Due to the lack of heavy industry in the city, the major source of REEs contamination in soils might be related to transportation and local agrotechnical and nutrition treatments. Our research on dandelion showed a decrease in REEs content in roots and leaves during autumn compared to spring. Slightly higher translocation efficiency was observed in autumn, possibly due to the plant’s age and tolerance mechanism.

PMID:40797018 | DOI:10.1038/s41598-025-15371-4

Dig Dis Sci. 2025 Aug 12. doi: 10.1007/s10620-025-09332-0. Online ahead of print.

ABSTRACT

BACKGROUND: Ulcerative Colitis (UC) is a chronic inflammatory colon condition with multifactorial causation. Recent interest has grown around the role of diet in managing symptoms and disease progression. Due to the absence of definitive evidence, no consistent dietary guidelines exist for UC management.

AIMS: This research investigates the impact of anti-inflammatory diets on disease activity and inflammation in patients with UC.

METHODS: PubMed, Cochrane, Embase and ClinicalTrials.gov were systematically searched from inception until April 2025. Dichotomous outcomes were pooled as risk ratio (RR) and continuous outcomes as mean differences (MD) with 95% confidence intervals (CI), using random-effects models. Heterogeneity was assessed using I² and X² statistics. Statistical analyses were performed using RevMan 5.4 and P<0.05 was considered significant.

RESULTS: Six RCTs involving 359 patients aged 12-75 years, having mild-to-moderate active UC, quiescent UC or UC in clinical remission, were included. Anti-inflammatory diets did not significantly affect the clinical remission (RR = 1.59, 95% CI [0.77 to 3.28], P = 0.21, I² = 76%) or relapse rates (RR = 0.62, 95% CI [0.30 to 1.25], P = 0.18, I² = 0%) in quiescent UC. However, improved clinical response was observed (RR = 1.82, 95% CI [1.20 to 2.75], P = 0.004, I² = 0%) in active disease. Moreover, FCP levels were significantly reduced (MD = – 210.01, 95% CI [- 359.77 to – 60.25], P = 0.006, I² = 0%), particularly during remission.

CONCLUSION: Anti-inflammatory diet complements conventional therapy in UC by promoting clinical response in active disease and reducing intestinal inflammation during remission.

PMID:40797000 | DOI:10.1007/s10620-025-09332-0